Analysis of Capabilities of the Multispectral Optical Method in Monitoring the Forest Territories

Q3 Mathematics
M. L. Belov, A. Belov, V. Gorodnichev, S. V. Alkov
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引用次数: 0

Abstract

The paper analyzes possibilities of the multispectral remote optical method in monitoring the forest areas. Results of mathematical simulation are provided of classification of the forest areas elements in the created neural network using experimentally measured reflection of the forest vegetation coefficients. It is demonstrated that the created neural network ensures high probability of correct classification within the classification problem (according to the multispectral remote optical monitoring data) of the forest probed areas. The selected spectral probing channels in a wide spectral range of ~ 400--2400 nm and the created neural network used seven spectral channels in the visible and in the near infrared spectral range, as well as the active laser sensor to measure the trees height. They provided a probability of correct classification of the forest areas elements (green deciduous trees, green coniferous trees, dry deciduous and coniferous trees, swamps, pastures with different vegetation cover and different types of soils) of more than 0.74 and the probability of misclassification of the forest areas elements of less than 0.08. The multispectral remote optical method could be used in operational monitoring of the vast forest areas from an aircraft (light aircraft or unmanned aerial vehicle)
多光谱光学方法在森林区域监测中的能力分析
分析了多光谱遥感光学方法在林区监测中的可行性。利用实验测量的森林植被系数反射,对所建立的神经网络中的林区要素进行了分类,并给出了数学模拟结果。实验表明,所建立的神经网络在森林探测区域的分类问题(根据多光谱遥感光学监测数据)中保证了高概率的正确分类。选择了~ 400 ~ 2400 nm宽光谱范围的光谱探测通道,建立了神经网络,利用可见光和近红外光谱范围内的7个光谱通道,以及主动激光传感器测量树木高度。他们提供的林区要素(绿色落叶乔木、绿色针叶树、干燥落叶针叶树、沼泽、不同植被覆盖的牧场和不同土壤类型)的正确分类概率大于0.74,林区要素的错误分类概率小于0.08。多光谱远程光学方法可用于飞机(轻型飞机或无人驾驶飞行器)对广大森林地区的操作监测。
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来源期刊
CiteScore
1.10
自引率
0.00%
发文量
40
期刊介绍: The journal is aimed at publishing most significant results of fundamental and applied studies and developments performed at research and industrial institutions in the following trends (ASJC code): 2600 Mathematics 2200 Engineering 3100 Physics and Astronomy 1600 Chemistry 1700 Computer Science.
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